Data Science and Machine Learning for Business Professionals

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Overview

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Based on the best-selling book, Becoming a Data Head, by Alex J. Gutman and Jordan Goldmeier. This course provides learners with the foundational skills to think critically about data and turn insights into actionable decisions. It covers key areas in data science, statistics, and machine learning, helping learners analyze data confidently and communicate findings effectively in diverse professional settings. By taking this course, you'll build both technical knowledge and professional judgment in data-driven decision-making. You'll gain the ability to challenge assumptions, evaluate machine learning techniques, and apply statistical principles to real-world data to derive meaningful insights. What makes this course unique is its blend of theory and real-world applications. It equips you with a comprehensive understanding of data science and machine learning methods, while emphasizing practical strategies and effective communication to non-technical stakeholders. This course is designed for professionals, students, or aspiring data practitioners with a basic understanding of data concepts. Whether you're in business, technology, or analytics, the course is perfect for anyone looking to enhance their data analysis skills without requiring advanced math or programming knowledge. From Becoming a Data Head Copyright © 2021 by John Wiley and Sons Inc Indianapolis Indiana Used by arrangement with John Wiley and Sons Inc

Syllabus

What Is the Problem?

In this section, we learn to define business problems with clear objectives, identify affected stakeholders, and assess data readiness to ensure data projects deliver measurable value and avoid wasted resources.

What Is Data?

In this section, we define data as encoded information, classify data types using standard terminology, and differentiate observational and experimental data collection methods, establishing a foundation for accurate analysis and informed decision-making.

Prepare to Think Statistically

In this section, we develop statistical thinking by recognizing variation in data, applying skepticism to claims, and interpreting probabilities in context. These skills enable informed decision-making in business and everyday life.

Argue with the Data

In this section, we learn to critically assess data quality by questioning its origin, collection methods, and representativeness. We evaluate validity, detect bias and missing data, ensuring reliable insights for informed decision-making.

Explore the Data

In this section, we explore exploratory data analysis (EDA) to uncover insights, identify outliers and missing values, and interpret correlations while avoiding causation errors, enabling data-driven decisions through iterative, evidence-based discovery.

Examine the Probabilities

In this section, we explore probability notation, conditional reasoning, and common fallacies to enhance critical thinking about uncertainty. You will learn to interpret and challenge probabilistic claims in professional contexts with greater clarity and confidence.

Challenge the Statistics

In this section, we examine statistical inference by evaluating sample size, significance levels, null hypotheses, and assumptions of causality. You'll learn to challenge data claims and make informed, evidence-based decisions.

Search for Hidden Groups

In this section, we explore unsupervised learning to discover hidden patterns in unlabeled data, applying PCA for dimensionality reduction and K-Means clustering to identify natural groupings with practical applications in customer segmentation and media organization.

Understand the Regression Model

In this section, we explore linear regression as a foundational method for predicting numerical outcomes. We learn to implement least squares regression, evaluate performance using R-squared and residuals, and identify critical pitfalls like multicollinearity, omitted variables, and data leakage.

Understand the Classification Model

In this section, we explore classification models for predicting categorical outcomes using logistic regression, decision trees, and ensemble methods. Key concepts include evaluating performance with confusion matrices and avoiding pitfalls like data leakage and misinterpreted accuracy.

Understand Text Analytics

In this section, we transform unstructured text into numerical features using N-grams, word embeddings, and topic modeling. We apply Naïve Bayes for sentiment analysis, enabling actionable insights from customer feedback and textual data.

Conceptualize Deep Learning

In this section, we explore how artificial neural networks underpin deep learning, enabling complex tasks like image and language processing. We examine their structure, applications, and the ethical challenges of deploying opaque, black box models in real-world systems.

Watch Out for Pitfalls

In this section, we identify common data pitfalls such as survivorship bias, Simpson's Paradox, and algorithmic bias. You'll learn to apply proper train-test splits, detect regression to the mean, and avoid misleading conclusions in real-world data projects.

Know the People and Personalities

In this section, we explore how interpersonal dynamics and communication breakdowns impact data projects. By identifying personality types, recognizing red flags, and applying empathy, teams improve collaboration and achieve better outcomes.

What's Next?

In this section, we explore applying statistical thinking to real-world decisions, interpreting ML and AI results critically, and avoiding common data pitfalls. You'll gain the skills to drive informed, evidence-based change in complex environments.